APIRE for DevSecOps Leads: Priya's story

Why this hurts right now

Your codebase is leaving the building one tab-complete at a time.

Nobody is keeping count.

Your engineers ship 200 Copilot completions a day. You have no log of what proprietary code those completions saw. Today the answer to "did our codebase train someone else's model" is "we don't know."

1. The Samsung pattern, repeating quietly in your stack.

In 2023 Samsung engineers pasted internal source code into ChatGPT, the snippets showed up in OpenAI's training pipeline, and Samsung banned the tool internally. The headline was the ban. The lesson the rest of the industry took home was the leak. Three years later, every engineering org has Copilot, Cursor, or Claude Code wired into the IDE, and the volume of code crossing the wire is orders of magnitude higher than what Samsung's incident exposed. The leak surface is not the question. The audit trail is. If a regulator asks tomorrow which lines of your monorepo were sent to which provider in Q1, the honest answer for almost every engineering org is "we cannot reconstruct it."

2. The 98% you cannot see and cannot un-see.

98% of employees are using AI apps the security team has never sanctioned. In an engineering org that figure compounds: developers wire AI assistants into the IDE, into code review bots, into PR templates, into CI helpers, into local scripts that summarize log output. Each integration is one more tab in your firewall report and zero in your DLP inventory. The integration paths are not malicious. They are productive. Trying to ban them backfires within a quarter because shadow tools just move into the personal device. The only stable answer is to put the security control in front of the model traffic itself, so the developer keeps the productivity and you keep the audit trail.

3. The "did our codebase train someone else's model" question.

It is the question that surfaces in board meetings, customer security questionnaires, and acquisition due diligence. It is also the question that, today, you can only answer by pointing at vendor terms-of-service. APIRE turns it into a real answer. Multi-Word Pattern Protection catches proprietary identifiers, internal project names, and secret-shaped tokens as phrases, not as keywords. Data Leakage Fortress masks them with placeholders before the prompt leaves the perimeter, so the model still gets enough context to generate a useful completion but never sees the actual secret. You install it with one environment variable: change OPENAI_BASE_URL from api.openai.com to app.apire.io and every IDE assistant on your fleet routes through inspection. Latency overhead under 50ms. Developer experience unchanged.

What APIRE does for you specifically

Four layers, between the IDE and the model.

Devs keep the speed. You get the log.

Content Safety Shield 14 content safety categories across 32 languages on every prompt and completion. Means harmful or policy-violating content, even in a code-comment payload, is flagged before it reaches the model and recorded in the audit log.
AI Threat Protection Shield 27+ AI-specific threats blocked at the gateway, including prompt injection through pasted documents, indirect injection through RAG context, jailbreaks, and Shadow AI usage. Detection list updates weekly because the attack list updates weekly.
Multi-Word Pattern Protection Context-aware detection of multi-word proprietary phrases: internal project names, customer identifiers, schema tokens. Catches "Project Atlas Q2 forecast" as a phrase, not three innocent words. This is the layer that stops your codebase from being summarizable in someone else's training set.
Data Leakage Fortress 950+ pre-configured DLP rules covering AWS keys, GitHub tokens, API credentials, PII, PHI, PCI. Inline masking with placeholders so the LLM still gets context but never the real secret. Zero retention in transit. One environment variable to install. Full developer-facing audit trail.

The Last Independent Champion

Lakera went to Check Point. Protect AI went to Palo Alto. Prompt Security went to SentinelOne. For a DevSecOps lead that means an acquired vendor's threat detections ship on a quarterly release train, not weekly. When a new prompt-injection variant lands on Twitter on a Tuesday, you cannot wait until Q3 for the parent platform's release window. APIRE ships detections on the cadence the threats arrive.

Ready to be APIRE's first DevSecOps lead partner?